Datahub::Factory - A CLI tool that transforms and transports data from a data source to a data sink.
# From the command line
$ dhconveyor <command> OPTIONS
# Transport data via a pipeline configuration file, without output
$ dhconveyor transport -p pipeline.ini
# Pretty output
$ dhconveyor transport -p pipeline.ini -v
# Show logging output
# Log levels: 1 - 3
$ dhconveyor transport -p pipeline.ini -L 3
# Only process the first 5 records
$ dhconveyor transport -p pipeline.ini -n 5
# Breaking up the pipeline configuration file in separate files
$ dhconveyor transport -g general.ini -i importer.ini -f fixer.ini -e exporter.ini
# Pushing a JSON file to a search index (Solr)
$ dhconveyor index -p solr.ini
# Pretty output
$ dhconveyor index -p solr.ini -v
# Show logging output
# Log levels: 1 - 3
$ dhconveyor index -p solr.ini -L 3
This package implements a command line ETL (Extract, Transform, Load) toolkit written as a wrapper around Catmandu and its ecosystem of Perl modules.
Features:
- Configuration files as ETL pipelines. The Catmandu command takes input via CLI options and arguments that are defined in its modules. Depending on the invocation, commands can end up with a long list of parameters containing potentially sensitive information. Sequestering that information in separate files allows users to approach pipelines as a configuration management concern.
- Conditional transforming. Pipelines can define multiple Catmandu Fixes. A check on a context dependent value (i.e. a repository field) allows the toolkit to dynamically apply the correct fix at runtime.
- Loose coupling with the Catmandu ecosystem. Wrapping Catmandu modules brings dependency inversion. This makes it easier to swap out Catmandu modules for something else without touching the infrastructure configuration.
- Robust processing with an increased fault-tolerance. Invalid records or input will simply be logged, rather then halting the entire process.
- Extensibility. Leveraging a modular approach, this toolkit can be expanded by custom modules for specific use cases.
Datahub::Factory fetches data from several sources as specified by the Importer settings, executes a Catmandu::Fix and sends it to a data sink, set via an Exporter. Several importer and exporter modules are provided out of the box, but developers can extend the functionality with their own modules.
Datahub::Factory supports Log4perl.
All commands share the following switches:
-
--log_level --L [int]
Set the log_level. Takes a numeric parameter. Supported levels are: 1 (WARN), 2 (INFO), 3 (DEBUG). WARN (1) is the default.
-
--log_output
Selects an output for the log messages. By default, it will send them to STDERR (pass STDERR as parameter), but STDOUT (STDOUT) and a log file.
-
--verbose -v
Set verbosity. Invoking the command with the --verbose, -v flag will render verbose output to the terminal.
-
--number -n [int]
Set number of records to process. Invoking the transport command with the --number, -n flag will process the first [int] records instead of all records available at the data source.
Documentation about command line options.
Fetch data from a local or remote source, convert the data to a target format and structure and export the data to a local or remote data sink.
Fetch data from a local source, and push it to an enterprise search engine in bulk. Currently only supports Apache Solr (https://lucene.apache.org/solr/)
Pipelines are defined in configuration files which are formatted according to the INI structure as expected by the Config::Simple library. Any pipeline consists of 4 parts: a General block, an Importer block, a Fixer block and an Exporter block.
Examples can be found in https://github.com/thedatahub/Datahub-Factory-Pipelines.
A simple example that pushes OAI data to a YAML output on STDOUT:
[General]
id_path = administrativeMetadata.recordWrap.recordID.0._
[Importer]
plugin = OAI
[plugin_importer_OAI]
endpoint = https://datahub.vlaamsekunstcollectie.be/oai
handler = +Catmandu::Importer::OAI::Parser::lido
metadata_prefix = oai_lido
[Fixer]
plugin = Fix
[plugin_fixer_Fix]
file_name = '/home/foobar/datahub.fix'
[Exporter]
plugin = YAML
[Exporter_YAML]
Note: The datahub.fix file is required, but can be left empty.
An example defining multiple fix transforms based on a context dependent value:
[General]
id_path = 'administrativeMetadata.recordWrap.recordID.0._'
[Importer]
plugin = OAI
[plugin_importer_OAI]
# endpoint = 'http://collections.britishart.yale.edu/oaicatmuseum/OAIHandler'
endpoint = https://datahub.vlaamsekunstcollectie.be/oai
handler = +Catmandu::Importer::OAI::Parser::lido
metadata_prefix = oai_lido
[Fixer]
plugin = Fix
[plugin_fixer_Fix]
condition_path = '_metadata.administrativeMetadata.0.recordWrap.recordSource.0.legalBodyName.0.appellationValue.0._'
fixers = MSK, GRO
[plugin_fixer_GRO]
condition = 'Musea Brugge - Groeningemuseum'
file_name = '/Users/foobar/groeninge.fix'
[plugin_fixer_MSK]
condition = 'Museum voor Schone Kunsten Gent'
file_name = '/Users/foobar/msk.fix'
[Exporter]
plugin = YAML
[plugin_exporter_YAML]
Note: condition_path contains the Fix path to the node that contains the context-dependent value. The condtion parameter in each fixer contains the value against which the conditional check is performed.
Datahub::Factory leverages a plugin-based architecture. This makes extending the toolkit with new functionality fairly trivial.
New commands can be added by creating a new, separate Perl module that contains a `command_name.pm` file in the `lib/Datahub/Factory/Command` path. Datahub::Factory uses the Datahub::Factory::Command namespace and leverages App::Cmd internally.
New Datahub::Factory::Importer, Datahub::Factory::Exporter, Datahub::Factory::Fixer, Datahub::Factory::Indexer plugins can be added in the same way.
- Matthias Vandermaesen
[email protected] - Pieter De Praetere
[email protected]
This software is copyright (c) 2016, 2019 by PACKED, vzw, Vlaamse Kunstcollectie, vzw.
This is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, Version 3, June 2007.